Bureau's Graph Identity Network (GIN) is a dynamic knowledge graph that links and correlates identity signals across users, devices, emails, and IP addresses to detect hidden fraud rings and money mules. The product aggregates data from multiple sources to uncover relationships and behavioral patterns that indicate coordinated financial crime. It generates adaptive risk scores based on real-time user assessment and chains data points to evaluate transaction risk instantly. Core capabilities include: - User Segmentation: Segments legitimate users from fraud rings using graph-based shared traits, industries, or linkages - Fraud Syndicate Detection: Identifies connected clusters of devices and identities to expose coordinated financial crime networks - Verification History: Tracks identity verification across devices, sessions, and industries to support explainable risk decisions - Real-time monitoring of user activity to decline unauthorized transactions and minimize fraud losses GIN is designed to help businesses comply with anti-money laundering (AML) regulations by identifying mule accounts, blocking repeat offenders, and eliminating high-abuse vectors such as promotional abuse and account takeover. The product is positioned as a shared intelligence network, providing access to cross-industry identity signals that individual businesses cannot build independently. It targets financial services, fintech, crypto, eCommerce, gaming, marketplaces, platform economy, and online dating industries.